Professor Parinya Chalermsook from Aalto University has received both Academy of Finland funding worth altogether almost 0.9 million euros and an ERC Starting Grant equal to more than 1.4 million euros. Both projects last 5 years and aim to revolutionize the theory of algorithms and optimization to meet the demands of real-world problems presenting simultaneously the challenges of uncertainty, optimization, and dynamic data.

“There are multiple theories and studies in algorithmics during the past three decades that are built independently and inconsistently. Many existing techniques in algorithmics are either tailored to very restrictive special cases or have reached their limitations. We aim to unify them, and to move towards understanding efficient computation better, with the support of recently developed theories such as fine-grained computational complexity”, explains Professor and Academy Research Fellow Parinya Chalermsook.

The projects attempt to seek new interplay across multiple areas of algorithmics, such as approximation algorithms, online algorithms, exponential-time algorithms, and data structures.

Multiple challenges to solve simultaneously

Real-world optimization problems pose a number of simultaneous challenges for the design of algorithms. For one, uncertainty of the users’ requests calls for designs that can deal with all eventualities and react with only partial visibility to future requests.

“Furthermore, even if we knew all the user requests in advance, it is in many cases difficult to compute an optimal and efficient way to handle all those requests. Therefore, with increasing amounts of input to process, we might need to settle with sub-optimal solutions”, continues Chalermsook.

One further challenge is the dynamic input that keeps changing over time. For this, there is a need to maintain efficient data structures to deal with the users’ changing requests and preferences.

Chalermsook’s areas of research are algorithms and complexity---both efficient computing and charting computational problems that cannot be efficiently solved.